Search Results (451)

As an innovative metaheuristic algorithm, Genghis Khan Shark Optimization (GKSO) faces challenges, including a tendency towards local optima and poor convergence speed and accuracy. To mitigate these limitations, an improved Genghis Khan shark optimizer (IGKSO) is proposed in this paper. A population partitioning method based on cosine similarity and fitness is introduced, where individuals are strategically assigned to different evolutionary phases: Disadvantaged populations are responsible for the foraging stage. By contrast, advantaged populations dominate the moving stage. In the moving stage, the base vector is randomly selected from multiple candidates, which ensures the evolutionary direction of the population while maintaining its diversity. An adaptive step-size mechanism is introduced to avoid boundary overflow problems. A subspace method is employed to prevent diversity loss during foraging. Additionally, in the hunting stage, a novel opposition-based learning strategy is proposed to moderate the tendency of converging to suboptimal solutions. Furthermore, during the self-protection phase, a criterion for assessing the diversity of the whole population is employed to monitor and supplement diversity in real time. The results of the CEC2017 and CEC2019 benchmark test sets reveal that IGKSO exhibits substantial advantages over the GKSO algorithm and eight other high-performance algorithms in terms of convergence speed and accuracy. Full article

Stepped bonded repair is widely used to restore load-carrying capacity in damaged composite structures, yet conventional circular-patch configurations require repair footprints that are frequently prohibited by spatial and geometric constraints in service environments. This study proposes an elliptical stepped repair strategy in which the patch axes are independently sized to accommodate directional space restrictions while preserving effective load transfer. A parametric three-dimensional finite element framework incorporating a Hashin-based progressive damage model and a cohesive-zone traction–separation law is developed and validated against both in-house lap-joint tests and an independent stepped-repair benchmark from the literature (discrepancy < 10%). Systematic variation in the elliptical geometry reveals that the major axis—oriented along the loading direction—is the dominant geometric parameter controlling strength recovery and failure mode: insufficient major-axis length results in premature adhesive debonding, whereas an appropriately sized major axis shifts failure to parent-laminate fracture and raises the ultimate load by up to 20% relative to a circular repair of equal minor-axis dimension. The minor axis plays a secondary but non-trivial role, and a synergistic optimum is identified at the 40–90 mm (minor–major) configuration. Regarding step partitioning, a four-step arrangement consistently maximizes ultimate load across all tested geometries due to the competition between transition-gradient smoothness and step-edge stress concentration density. Finally, an external woven overlay is shown to both improve and equalize strength across geometrically distinct repairs by suppressing interfacial stress concentration and engaging a global cooperative failure mode. These findings establish design guidelines for elliptical stepped repairs under engineering space constraints. Full article

The distinct co-occurrence of soil water content (SWC) and vapor pressure deficit (VPD) influences ecosystem water use efficiency (WUE) by modifying the synergistic relationship between gross primary productivity (GPP) and evapotranspiration (ET), yet how they impact each other remains unclear in agricultural ecosystems. Based on long-term eddy covariance flux data (2005–2014) observed at a rainfed maize site in Northeast China, we examined how SWC and VPD affect WUE by decomposing it into gross primary productivity to transpiration ratio (GPP/T) and transpiration to evapotranspiration ratio (T/ET). Results showed that WUE was more sensitive to VPD than SWC. Increasing VPD directly suppressed WUE under all soil moisture conditions, whereas SWC had a context-dependent effect: higher SWC reduced WUE under low VPD but enhanced WUE under high VPD. The underlying mechanism was that changes in GPP/T (plant physiological regulation) dominated the WUE responses to both SWC and VPD (contributing 70.25–83.30% and 67.89–87.96%, respectively), while T/ET (evapotranspiration partitioning) played a minor role (<18%). Therefore, to improve WUE under future drier climates, agronomic practices should focus on enhancing photosynthetic capacity and stomatal regulation (e.g., selecting drought-tolerant varieties, optimizing nitrogen supply) rather than solely reducing soil evaporation. Furthermore, supplemental irrigation applied specifically during periods of high VPD (when atmospheric demand is strong) can effectively enhance WUE, as soil moisture becomes critically beneficial under such conditions. These findings provide a mechanistic basis for improving water use efficiency in rainfed maize systems under climate change. Full article

Chaotic signals commonly exhibit nonlinear and nonstationary characteristics, while noise contamination reduces signal interpretability and degrades subsequent feature extraction and dynamical analysis. To improve the stability of mode-boundary determination and mitigate reconstruction distortion, this paper proposes a hybrid denoising framework that integrates feature mode decomposition (FMD), amplitude-aware permutation entropy (AAPE), dual-tree complex wavelet transform (DTCWT), and Savitzky–Golay (SG) filtering. First, the noisy signal is decomposed into multiple mode components using FMD. Then, the AAPE of each mode is calculated to adaptively distinguish high-frequency noise-dominant modes from non-high-frequency modes. For the high-frequency noise-dominant modes, improved logarithmic threshold shrinkage is applied to the magnitudes of DTCWT complex coefficients to suppress random noise and reduce threshold-induced bias. For the non-high-frequency modes, SG filtering is employed to further attenuate residual noise while preserving local waveform structures. Finally, the processed modes are reconstructed to obtain the denoised signal. Experiments on a simulated Lorenz chaotic signal and a real-world sunspot time series demonstrate that, across different noise levels, AAPE provides more stable mode partitioning than ApEn, CC, and CMSE. Moreover, under Gaussian white noise, Poisson noise, and uniform noise, the proposed method generally achieves a higher output signal-to-noise ratio (SNR) and a lower root mean square error (RMSE) than WT, CEEMD, EEMD, CEEMDAN+LMS, and VMD, while also yielding better performance in phase-space reconstruction and temporal-detail recovery. These results verify the effectiveness and practical applicability of the proposed method for chaotic signal denoising. Full article

Lactococcosis caused by Lactococcus garvieae is an emerging threat to warmwater aquaculture, yet evidence integrating field outbreaks with robust molecular confirmation and controlled virulence testing remains limited for Thailand’s cage-cultured tilapia. From May to October 2025, acute mortality events were investigated in cage-cultured Nile tilapia (Oreochromis niloticus) in a reservoir in Ubon Ratchathani Province, Thailand. Suspected outbreaks were defined by abrupt daily mortality exceeding 5% accompanied by septicemia-like clinical signs. Water quality during sampling covered the following ranges: temperature 28.6–31.9 °C, pH 6.5–7.0, salinity 0.02–0.03 ppt, electrical conductivity 0.036–0.046 mS/cm, TDS 22.20–26.50 mg/L, total alkalinity 17.0–34.0 mg/L as CaCO₃, total hardness 12.0–60.0 mg/L as CaCO₃, dissolved oxygen 6.5–7.0 mg/L, and NH₃ were below the limit of detection. Full-length 16S rRNA tissue profiling revealed strong tissue partitioning: blood microbiomes were consistently dominated by Lactococcus and L. garvieae at the species level, whereas gills showed higher richness and mixed communities with multiple opportunistic taxa. Culture isolation was more reliable from blood than gills, yielding 16 Gram-positive, catalase-negative isolates (AAHM-LG2501–AAHM-LG2516) that clustered within the L. garvieae clade in near full-length 16S rRNA phylogenetic analysis and were separated from closely related Lactococcus lineages. A representative blood isolate (AAHM-LG2501) showed dose-dependent virulence in controlled challenges, with an LD₅₀ of ~1.05 × 10⁵ CFU/fish by intraperitoneal injection and an LC₅₀ of ~1.20 × 10⁶ CFU/mL by immersion. Histopathology supported systemic dissemination, with injection producing more consistent multi-organ lesions than immersion, particularly in head kidney, liver, and spleen, while gills exhibited route-associated epithelial and vascular alterations. Together, these findings confirm L. garvieae as a major etiological agent of septicemic outbreaks in cage-cultured tilapia in Thailand and support a practical surveillance framework prioritizing blood sampling, molecular confirmation, and risk-based monitoring to guide biosecurity and vaccine-oriented prevention. Full article

Hydrological fluctuations drive community dynamics in floodplain lakes, yet their integration into water resource management remains limited. Here, we integrated environmental DNA (eDNA) metabarcoding with hydroacoustic surveys to investigate vertebrate community assembly in China’s largest freshwater lake (Poyang Lake) during the winter dry season. We detected 65 vertebrate species, with Cypriniformes dominating. Beta-diversity partitioning revealed that turnover dominated taxonomic and functional dissimilarity, while phylogenetic beta diversity was characterized by nestedness, which is consistent with environmental filtering. Functional richness declined with water depth, coinciding with hydroacoustic vertical size stratification, indicating niche partitioning along depth gradients. Null model analysis showed stochastic processes (ecological drift) dominated regional assembly (72.97%), whereas joint species distribution modeling attributed explained variation to environmental factors (28.9%, notably water depth) and species associations (29.7%) at local scales. This hierarchical framework, regional stochasticity shaping the species pool and local deterministic filtering structuring communities, reframes environmental flow conceptualization: effective management must preserve the full spectrum of hydrological variability and maintain bathymetrically diverse habitats that support functional niche differentiation. The integrated eDNA-hydroacoustic approach offers a non-invasive, high-resolution toolkit for biological assessment within regulatory water quality frameworks. Full article

Soil moisture (SM) is a pivotal state variable of the terrestrial hydrosphere, modulating energy partitioning, agricultural productivity and extreme-event propagation. This study analyzes 43 years (1982–2024) of data to assess soil moisture (SM) dynamics in the Yellow River Basin (YRB). Results indicate a statistically significant basin-wide SM decline across weekly, monthly, and annual scales, with grid-scale slopes ranging from −2.26 × 10⁻⁴ to 8.32 × 10⁻⁵ m³ m⁻³ month⁻¹. Spatially, non-farm areas retain higher SM than cultivated lands, with a distinct upstream-to-downstream variability pattern. While alpine headwaters show moistening, pervasive drying characterizes mid- and lower-catchments. Critically, transitional landscapes are approaching tipping points, risking shifts into persistently wetter or drier stable states where minor perturbations could lock ecosystems into new conditions. This underscores the urgent need for targeted climate-adaptation interventions. Generalized additive modeling identifies surface net solar radiation, soil temperature, and vapor pressure deficit as dominant drivers across multiple temporal scales. Their respective contributions, averaged across the basin, accounted for 29.4%, 25.3%, and 23.0% of the explained variance. Additionally, actual evapotranspiration emerged as a significant driver on the weekly scale, particularly within the center of the basin. These findings enhance process-based understanding of SM variability and provide a scientific foundation for adaptive water-resource management in the YRB. Full article

Industrial symbiosis (IS) research has documented many successful ecosystems but still lacks an empirically grounded typology linking resource flow configurations to environmental outcomes across diverse contexts. This study develops such a typology and tests whether distinct configurations achieve comparable environmental performance through different pathways—the configurational principle of equifinality. Drawing on 68 documented IS ecosystems across 48 countries, we apply k-means clustering to five flow-intensity dimensions—material, energy, water, logistics, and knowledge—and characterise the resulting partition using one-way ANOVA, Tukey HSD post hoc tests, multinomial logistic regression, and a Cox proportional-hazards model. Four configurations emerge: a dominant low-flow group (n = 34) and three coordinated configurations—energy–knowledge (n = 11), material-dominant (n = 16), and water-oriented (n = 7). The three coordinated configurations all significantly outperform the low-flow group on environmental performance (F(3, 57) = 11.60, p < 0.001), with effect sizes very similar and no significant differences among them, providing direct empirical evidence for equifinality. Economic performance does not differ significantly across configurations, and the multinomial model of contextual predictors is jointly insignificant—a pattern we read as consistent with equifinal contextual pathways rather than as a methodological flaw. Robustness checks across alternative clustering algorithms, operationalisations, and sub-samples support the typology’s stability. This study contributes an empirically grounded framework for circular economy practice that moves beyond one-size-fits-all prescriptions and offers a configurational lens for the design of sustainable industrial ecosystems. Full article

Ayu (Plecoglossus altivelis) is an East Asian amphidromous river fish, yet seasonal microbiota dynamics remain unclear. We investigated ayu in the Dajing Stream (Zhejiang Province, China) by synchronously sampling water microbiota (H), gut content microbiota (N), and gut tissue-associated microbiota (C) across four seasons. Each season, four fish were collected, and an overlapping pooling strategy (abc/abd/bcd) generated three composite replicates for C and N (n = 3 composites/season); water was collected as three field replicates (n = 3/season), yielding 36 samples (12 per niche). Using 16S rRNA amplicon sequencing and COI barcoding of stomach contents, we observed the clearest seasonal differentiation in H and seasonal variation in N consistent with diet shifts, whereas C was comparatively stable. COI signals indicated a diet dominated by aquatic insects in spring/summer, which shifted toward smaller prey (e.g., rotifers) in winter. Together, these results highlight strong niche partitioning and season-linked shifts in water and gut content communities relative to the more stable tissue-associated microbiota. These findings should be interpreted as exploratory and require validation in larger individual-level studies. Full article

Reconciling hydropower development with aquatic biodiversity conservation is a central challenge for sustainable river management worldwide. Cascade dam configurations, in which multiple impoundments are arranged in series along a single channel, impose longitudinal environmental gradients that restructure biological communities across trophic levels. Whether the resulting multi-trophic responses are independently driven by shared abiotic gradients (environmental filtering) or mechanistically coupled through direct food-web interactions (trophic cascading) remains unresolved. We surveyed phytoplankton, zooplankton, and benthic macroinvertebrates simultaneously at seven stations along a 430 km gradient downstream of Danjiangkou Dam in the Hanjiang River, the largest tributary of the Yangtze River and the source of China’s South-to-North Water Diversion Middle Route, over eight seasonal campaigns (2015–2017). Variance partitioning, piecewise structural equation modeling, Mantel tests, and co-occurrence network analysis were applied to partition environmental and trophic pathways. Environmental filtering dominated community restructuring at all three trophic levels, while the biotic proxy for direct trophic interactions explained less than 0.4% of community variation, consistent with weak detectable trophic coupling at seasonal resolution. Distance from Danjiangkou Dam shaped downstream transparency and turbidity gradients that mediated trophic-level-specific responses along distinct environmental axes (pH and water temperature for phytoplankton, conductivity for zooplankton, and transparency for benthic macroinvertebrates). Benthic macroinvertebrates were systematically decoupled from the pelagic analytical framework, absent from the cross-trophic co-occurrence network and structured more by spatial configuration than by water-column variables. Hub species in the network were associated with downstream mineralized conditions, confirming that network architecture reflects shared environmental preferences rather than biotic interactions. These findings support a management shift from single-dam mitigation toward cascade-scale coordination of environmental flow regimes, sediment connectivity, and substrate restoration as integrated strategies for sustaining multi-trophic biodiversity in regulated rivers. Full article

Multimodal multi-objective optimization problems often exhibit one-to-many mappings, where multiple distinct variable vectors correspond to the same objective vector. This characteristic makes Pareto set (PS) estimation difficult, as conventional inverse modeling approaches assume a one-to-one correspondence. This study proposes a cluster-wise PS estimation framework in the variable space. Known solutions are partitioned into locally monotonic clusters using oscillation detection with an amplitude threshold, and independent response surface models are constructed for each cluster. By estimating PS solutions from multiple cluster-specific models for a given direction vector, the method preserves multimodal structures that conventional approaches fail to capture. Numerical experiments on the multimodal benchmark problems MMF1–8 and LIRCMOP1–2 demonstrate that the proposed method achieves equal or better HV and IGD values than the conventional method, while improving decision-space approximation as measured by IGDX in most test cases and suppressing the generation of dominated solutions. Full article

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

Excavation adjacent to operating urban rail transit faces formidable deformation control challenges. To address this, a parametric collaborative optimization framework integrating micro steel pipe pile isolation and temporary intermediate partition wall reinforcement is proposed. Taking a foundation pit project at Shangdi Station of Beijing Metro Line 13 as a case study, a three-dimensional finite element model was established using the Hardening Soil constitutive model and calibrated with field monitoring data. Optimization analysis reveals that micro-pile spacing is the dominant factor controlling local rail settlement, while intermediate partition wall thickness primarily dictates global surface settlement. By balancing stringent safety limits with construction economy through a multi-objective evaluation, the preferred support configuration was calculated to be 273 mm diameter micro-piles at 500 mm spacing, combined with a 300 mm-thick partition wall. This collaborative configuration successfully truncates lateral soil displacement, reducing maximum rail settlement by over 55% and surface settlement by 53.6% compared to the baseline. Field monitoring results show high consistency with the numerical predictions (RMSE = 0.1438 mm), confirming the reliability of the proposed parametric collaborative optimization framework. Ultimately, this framework provides a validated, quantitative design methodology and a practical reference for support design in constrained excavations adjacent to existing sensitive infrastructure. Full article

Madeira’s agricultural sector is characterised by pronounced structural heterogeneity, land fragmentation, and increasing socio-economic and environmental pressures. However, comprehensive typological and spatial analyses remain limited, particularly in small island contexts. This study addresses this gap by providing a typological and spatial analysis of the agrarian structure in the Autonomous Region of Madeira, Portugal, using 2019 Agricultural Census data. An integrated framework combining Principal Component Analysis (PCA), Partitioning Around Medoids (PAM) clustering, and Random Forest validation—representing a novel approach in agrarian typology studies—is employed to identify three agricultural models: Intensive Subtropical Agriculture (24.1% of parishes), characterised by small holdings and high labour intensity; Extensive Traditional Agriculture (64.8%), featuring moderate farm size and diversified cropping; and Pasture-based Agriculture (11.1%), dominated by larger farms and low labour input. The results confirm significant structural trade-offs, including a strong inverse relationship between farm size and labour intensity (r = −0.653) and a negative correlation between specialisation and crop diversity (r = −0.673). Spatially, the models exhibit clear territorial differentiation, with subtropical systems concentrated in southern coastal areas and traditional systems prevailing in northern and interior regions. These findings support the hypothesis of a hybrid agrarian transition. Despite relying on cross-sectional data, the results provide a robust basis for targeted and place-based policy design within the Common Agricultural Policy (CAP) framework. Full article

This research reveals the coupling mechanism between structural deformation and hydrocarbon accumulation. The Dibei area in the Kuqa Depression represents a key hydrocarbon exploration domain within the northern Tarim foreland basin. Although extensive studies on stratigraphy, sedimentology, and accumulation mechanisms have been conducted, the control of segmented deformation on traps remains poorly understood. Furthermore, the synergistic regulation mechanism involving paleo-uplifts, salt thickness, synsedimentation, and erosion is still ambiguous. Based on high-quality 2D and 3D seismic data, this study integrates tectonic evolution balanced restoration with physical modeling. We conducted two sets of 3D sandbox experiments: “differential paleo-uplift and salt thickness” and “synsedimentation-erosion.” This approach systematically investigates the control of tectonic evolution on trap formation. Results show a strong correspondence between the “subsalt–salt–supra-salt” structural deformation and trap types. The supra-salt layer is dominated by detachment fold traps, whereas the subsalt layer features thrust-fold anticline traps. The basement paleo-uplift governs structural segmentation and trap distribution. Salt thickness modulates strain partitioning and trap stability. Synsedimentation optimizes trap conditions via tectono-sedimentary coupling. Erosional unconformities serve dual functions as both migration pathways and seal beds. These four factors work synergistically throughout the entire petroleum system, from “trap formation–migration–accumulation–preservation.” It enriches the genetic theory of salt-related structures in foreland basins. The findings provide a reference for predicting favorable exploration zones, evaluating trap characteristics, and assessing resource potential in the Kuqa Depression. Full article